Medical visual question answering

ABSTRACT

Aspects of the invention include a computer-implemented method including extracting a domain-specific object feature from a first image data, wherein the feature describes an object in the first image data. A domain-specific semantic meaning of text data is determined. The object feature is mapped to a portion of the text data, wherein the portion of the text data describes the object. A joint representation of the object and the portion of the text data is created. A second image data and a query directed towards an object in the second image data is received. An answer to the query is generated based on the joint representation.

BACKGROUND

The present invention generally relates to programmable computingsystems, and more specifically, to programmable computers configured andarranged to perform medical visual question answering.

Computer-based visual question answering systems can receive a digitalimage and provide a response to a question about the image. A visualquestion answering system can be tasked with analyzing the question andsearching for objects in the image related to the question. Therefore,the computer visual question answering system has to analyze thequestions in relation to the content of the digital image. As such,computer visual question answering is a complex process that involvestextual analysis and visual analysis to determine an image and textrelationship through computer-based reasoning.

SUMMARY

Embodiments of the present invention are directed to visual questionanswering. A non-limiting example computer-implemented method includesextracting a domain-specific object feature from a first image data,wherein the feature describes an object in the first image data. Adomain-specific semantic meaning of text data is determined. The objectfeature is mapped to a portion of the text data, wherein the portion ofthe text data describes the object. A joint representation of the objectand the portion of the text data is created. A second image data and aquery directed towards an object in the second image data is received.An answer to the query is generated based on the joint representation.

Other embodiments of the present invention implement features of theabove-described method in computer systems and computer programproducts.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 illustrates a training phase for a visual question answeringsystem in accordance with one or more embodiments of the presentinvention;

FIG. 2A illustrates a training phase for object detection for a visualquestion answer system in accordance with one or more embodiments of thepresent invention;

FIG. 2B illustrates a training phase for semantic analysis for a visualquestion answer system in accordance with one or more embodiments of thepresent invention;

FIG. 3 illustrates a training phase for a visual question answeringsystem in accordance with one or more embodiments of the presentinvention;

FIG. 4 illustrates a training phase for a visual question answeringsystem in accordance with one or more embodiments of the presentinvention;

FIG. 5 illustrates a block diagram of components of a visual questionanswering system in accordance with one or more embodiments of thepresent invention;

FIG. 6 illustrates a flow diagram of a process for training a visualquestion answering system in accordance with one or more embodiments ofthe present invention;

FIG. 7 illustrates a cloud computing environment according to one ormore embodiments of the present invention;

FIG. 8 illustrates abstraction model layers according to one or moreembodiments of the present invention; and

FIG. 9 illustrates a block diagram of a computer system for use inimplementing one or more embodiments of the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagrams or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order, or actions can be added, deleted,or modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

DETAILED DESCRIPTION

One or more embodiments of the present invention providecomputer-implemented methods, computing systems, and computer programproducts for training a visual question answering system to generate ajoint representation of information received from different formats. Thejoint representation describes a relationship between the information,which can be used to generate an answer to a query about an image.

Healthcare professionals and patients are increasingly communicating viahealthcare portals and internet-enabled video conferencing. These newlines of communication and the ability to digitally store medicalrecords has enabled healthcare patients and medical students to havegreater access to medical imaging data. In each of these situations, thepatients or the students may have basic questions about the images, butlimited access to healthcare professionals. The patients and studentscan turn to visual question answering (VQA) systems. However, theaccuracy of answers generated by conventional VQA systems is limited bythe size of the dataset used to recognize objects in a target image.Conventional VQA systems generate inaccurate answers at a higherfrequency because they are trained with small training datasets. This isdue to the unavailability of large datasets for training VQA systems inthe healthcare space. Furthermore, in conventional VQA systems, thesystems are trained using image training data concatenated with textualtraining data. Concatenating the data leads to losing informationregarding the relationship of the image and the text.

One or more embodiments of the present invention address one or more ofthe above-described shortcomings by providing computer-implementedmethods, computing systems, and computer program products for a VQAsystem that includes an encoder that is trained using medical imagingdata and medical records. The herein described VQA system can combinedata presented via multiple modalities into a joint representation ofall of the data. The joint representation is based on a mapping betweenthe data derived from medical records and data derived from medicalimages. The herein described VQA system further includes a decoder thatis decoupled from the encoder, and therefore does not require additionalmedical records or images for training the encoder. The decouplingreduces the need for large-scale image-question-answering data. Rather,small-scale data can be used for training the decoder (i.e., image querydata with answer labels). This is because the joint representationpre-trained in a first training phase using medical records and medicalimages keeps a strong representation across the dual modalities.

Referring to FIGS. 1, 2, 3, and 4, a multi-phase process of training aVQA system 100 is described. As seen in FIG. 1, a first phase of thetraining is illustrated. The first phase is executed on a first module200, which includes an image embedder unit 102 and a text embedder unit104. The image embedder unit 102 is operable to receive an image 202 asa training instance and employ a model to identify objects in the image202. The image 202 can be, for example, an x-ray image, a CT scan, aphotographic image, or other medically related image data. The imageembedder unit 102 can employ a model that executes computer visiontechniques on the image 202 for object detection. Object detectionincludes both image classification and object localization. Imageclassification includes predicting a class of one or more objects in theimage 202. To perform image classification, the image embedder unit 102receives the image 202 as an input and outputs a class label in the formof one or more integer values mapped to class values. Objectlocalization includes identifying a location of the one or moreidentified objects in the image 202. To perform object localization, theimage embedder unit 102 can process the received image 202 and outputone or more bounding boxes, which define a spatial relationship of theobjects in the image 202. The image embedder unit 102 can be implementedthrough a neural network type architecture with input, hidden, andoutput layers. The image embedder unit 102 can be trained to detectobjects from a particular domain (e.g., medical domain) by adjusting theweights and biases of the neural network. The image embedder unit 102further uses the values of the weights and biases to generate an imageembedding vector 204 to represent the identified objects. The weightscontrol the signal between two neurons of a neural network. A weightdetermines the extent an input dictates an output. Biases are constantsthat are an additional input into the next layer of a neural network.Biases not determined by a previous layer of a neural network.Therefore, even if an output of a previous layer is zero, the biasensures that an input will be entered into a subsequent layer.

An exemplary embodiment, the image embedder unit 102 employs a trainedartificial neural network to execute the model, for example, aregion-based convolutional neural network (R-CNN), or other neuralnetwork appropriate for image analysis. The R-CNN generally operates inthree phases. First, the R-CNN analyzes the image 202, extractsindependent regions in the image 202, and delineates the regions ascandidate bounding boxes. Second, the R-CNN extracts features, forexample, using a deep convolutional neural network, from each region.Third, a classifier, for example, a support vector machine (SVM), isused to analyze the features and predict a class for one or more objectsin a region.

The text embedder unit 104 can receive a text document 206 in electronicform as a training instance and derive a semantic meaning of thedocument. The text document 206 can be an electronic medical record,physician's notes, journal article or other textual document. The textdocument 206 describes at least a portion of the image 202. For example,the text document 206 can be a transcription of a physician'simpressions of an x-ray, where the x-ray is the image 202. The textembedder unit 104 can apply natural language processing techniques, viaa model, to semantically analyze the text document 206. The model canbe, for example, a word embedding model. The text embedder unit 104 canreceive the text document 206 and segment it into passages (e.g.,paragraphs, sections, etc.). The text embedder unit 104 can furthersegment the passages into tokens (e.g., words and phrases). The textembedder unit 104 can retrieve individual passages and map the tokens inthe passage to respective words vectors in a low-dimensional space.Various techniques can be applied to derive a context of the textdocument 206. For example, the text embedder unit 104 can take a targetword for the embedding being learned and attempt to predict thesurrounding context words from it. In another embodiment of the presentinvention, the text embedder unit 104 analyzes the context of the wordssurrounding a masked target word and seeks to predict the target wordbased on the surrounding words. The text embedder unit 104 can beimplemented through a neural network type architecture with input,hidden, and output layers. The text embedder unit 104 can be trained tosemantically analyze text from a particular domain (e.g., medicaldomain) by adjusting the weights and biases of the neural network. Thetext embedder unit 104 further uses the values of the weights and biasesto generate a text embedding vector 208 to represent the semanticmeaning of the text document 206.

In some embodiments of the present invention, the text embedder unit 104can apply machine learning techniques to perform the semantic analysis.In an exemplary embodiment, the text embedder unit 104 employs a trainedartificial neural network, for example, a recurrent neural network(RNN), or other neural network appropriate for text analysis.

The image embedding vectors 204 and the text embedding vectors 208 arehigh-dimensional vectors that can be translated in a low-dimensionalembedding space. By representing the objects by the embedding vectors inthe low-dimensional space, the system 100 can combine the informationgenerated from different modalities, even if the vectors have differentdimensions. For example, the image 202 includes a large amount ofinformation describing pixel features. On the other hand, the textdocument 306 includes a relatively smaller amount of information relatedto the semantic meaning. By converting both the image 202 and the textdocument 206 into respective embedding vectors 204 208 within the sameembedding space, the system 100 can map the image embedding vector 204text embedding vector 208, regardless of the dimensional differences.

The image embedder unit 102 and the text embedder unit 104 can transmitthe image embedding vector 204 and the text embedding vector 208 to thesecond module 210, which includes a multi-modal encoder 212. It shouldbe appreciated that although the herein described figures describe twomodalities, image and text, the first module 200 is operable to receivedata from more than two modalities. For example, in some embodiments ofthe present invention, the first module 200 can receive auditory data,such as a microphone recording. Furthermore, the two describedmodalities are described as images and text for illustration purposes.In some embodiments of the present invention, the two modalities caninclude image data and audio data, rather than image data and text data.

Referring to FIGS. 2A and 2B, an illustration of the second phase of thepre-training is illustrated. The second phase includes training thesecond module 210 and permitting a trained second module 210 to makeinferences. The second phase is performed by the second module 210 whichlearns features found the image embedding vector 204 that relate tofeatures in the text embedding vector 208, and vice-versa. The secondmodule 210 includes a multi-modal encoder 212 for encoding arelationship between features from the image embedding vector 204 andthe text embedding vector 208. In some embodiments of the presentinvention, the multi-modal encoder 212 can be implemented asfeed-forward artificial neural network, for example, a multi-layerperceptron.

Referring to FIG. 2A, the multi-modal encoder 212 can receive the imageembedding vector 204 and be trained to recognize domain-specificfeatures. Various methods can be used to recognize the features, forexample, the multi-modal encoder 212 can employ masked feature model. Insome embodiments of the present invention, the masked feature model istrained to recognize features of a particular domain, for example, thehealthcare domain. This can be performed by adjusting the weights andbiases of the neural network to recognize healthcare domain-specificfeatures. The multi-modal encoder 212 can receive the image embeddingvector 204 and employ the masked feature model to predictdomain-specific features from the vector. The masked feature model canmask a feature such that it is not recognizable by subsequent layers ofthe neural network. The masked-layer model can receive the contextfeatures patches 216 218 surrounding the masked feature 214 as inputs togenerate a predicted masked feature 220 as to what the masked feature214 should be. It should be appreciated that although FIG. 3A onlyillustrate a first feature patch 216 and a second feature patch 218, inpractice, the masked feature model can receive greater than two featurepatches to make a prediction as to the masked feature 214.

Referring to FIG. 2B, the multi-modal encoder 212 can also receive thetext embedding vector 208 and be trained to derive a meaning from thevector. Various methods can be used to derive a context of the wordsdescribed by the text embedding vector 208, for example, the multi-modalencoder 212 can employ a masked language model. The multi-modal encoder212 can receive the text embedding vector 208 and employ the maskedlanguage model to segment the vector to describe respective passages onthe text document 206. Passages can include, for example, sentences,phrases, and bullet points. The masked language model masks a token suchthat it is not recognizable by subsequent layers of the neural network.The masked-layer model then uses the context tokens 224 226 surroundingthe masked token 222 to generate a predicted masked token 228 as to whatthe masked token 222 should be. It should be appreciated that althoughFIG. 2B only illustrates a first token 224 and a second token 226, themasked feature model can receive greater than two tokens to make aprediction as to the masked token 222.

Referring to FIG. 3, the multi-modal encoder 212 can be trained todetermine whether a relationship exists between the predicted maskedfeature 220 and the predicted masked token 228. In some embodiments ofthe present invention, the multi-model encoder 212 can employ animage-text matching model to determine the relationship between thepredicted masked feature 220 and the predicted masked token 228. Theimage-text matching model can determine a semantic relationship betweenan object described in the image embedding vector 204 and the wordsdescribed in the text embedding vector 208. The multi-modal encoder 212can employ the image-text matching model to generate a nature languagedescription of the objects in the image. As described above, themulti-modal encoder 212 can be trained to predict a word based onsurrounding words, and predict a feature based on surrounding features.Therefore, the multi-modal encoder 212 can predict match based on acontext of surrounding tokens and surrounding feature patches.

The multi-modal encoder 212 can employ an attention mechanism thatallows the encoder to have the ability to focus on a subset of tokens(or features). The attention module mechanism can be implemented on atwo-dimensional convolutional layer of a neural network, and include asigmoid function to generate a mask of the feature map of the embeddingspace. The attention mechanism can receive an a×b×c feature map as aninput and outputs a 1×b×c as an output attention map. The attentionmechanism then performs an element-wise multiplication on the attentionmap with the input feature map to get a more refined and highlightedoutput.

The multi-modal encoder 212 can employ the image-text matching model tomatch features from the image embedding vector 204 and tokens from thetext embedding vector 208. The image-text matching model can performthis function even though the image embedding vector 204 the textembedding vector 208 have different dimensions. The multi-modal encoder212 can employ the image-text matching model to map the features andtokens into a same vector space and determine a match or not a match400. The multi-modal encoder 212 can employ a classifier that is trainedto determine whether an text matches an image or an object in an image.For example, if a token describes a liver and an image feature describesa liver, the classifier can be trained to determine a match exists. If,however, a token describes a broken arm and an image feature describesan ear, the classifier can be trained to determine that there is nomatch. The multi-modal encoder 212 can also employ the image-textmatching model to generate pairs of tokens (or sets of tokens) andfeatures (or sets of features) and determine a probability that thepairs are a match or not a match. A match suggests that the token or setof tokens describes the object described by a feature or set offeatures.

Referring to FIG. 4, a third phase of the pre-training is illustrated.The multi-modal encoder 212 can generate an image-text representation500 in the form of a high dimensional vector. The image-textrepresentation 500 is based on a matching of a token (or set of tokens)and an image feature (or set of image features). The matching featuresfrom the image embedding vector 204 are mapped to matching tokens fromtext embedding vector 208. The mapping is used to generate a jointimage-text representation 500 describing a contextual representation ofboth image and text. Therefore, rather than concatenating an imageembedding vector and a text embedding vector, the multi-modal encoder212 generates a joint image-text representation 500 of the matchingtokens and features.

Conventional concatenation is a simple and rough combination of imageand text embeddings. The concatenated image and text embedding do notdescribe the relationship between image patches and the correspondingtext tokens. All the images patches and text tokens share the same anduniform weights to generate the answer tokens, which leads to inaccurateanswer generation. The herein described computer-implemented methods,computing systems, and computer program products use adaptive weightswhen generating the answer tokens. In detail, for a single step, ananswer token is generated, the weights of different image patches andtext tokens are different. For the next step when generating anotheranswer token, the weights are changed adaptively. In other words, theweights assigned to different image patches, different text tokens, anddifferent time step are all different. The weights can be referred to asthe “attention vector/attention map”, which quantifies how much theimage-text matching model “pays attention to” each image patch and texttoken when generating an answer token. The attention weights aredetermined during the training of image-text matching model and lead tomore accurate answer generation than conventional methods.

The third phase of the pre-training includes generating answers toqueries. The multi-modal encoder 212 can transmit the image-textrepresentation 500 to an answer decoder 502. The answer decoder 502 canreceive a training query 112 and an image 110 as inputs and generate ananswer prediction 504. The answer decoder 502 can be implemented by aneural network. The answer decoder 502 can further be in the form of asequential generating model, such a long short-term memory (LTSM) ortransformer decoder. An LSTM network is a form of a recurrent neuralnetwork (RNN) capable of learning order dependence in sequenceprediction problem. A transformer decoder is another sequence learningneural network. The answer prediction 504 can be retrieved from adatabase 116 and provided in natural language. During the third trainingphase, a determination is made whether the answer prediction is corrector incorrect. The answer decoder 502 can be trained through supervisedlearning by matching an answer prediction 504 with an answer label 506provided in a training set of answers. The answer decoder 502 can map acorrect answer to an associated image-text representation 500.

In some embodiments of the present inventions, the encoding is decoupledfrom the decoding. The first module 200 is trained using images and textdata that are readily available in large quantities. For example,medical images and medical records are readily available to formtraining instances. The first module 200 and the second module 210 aretrained using the image and text training instances. However, answerdecoder 502 receives the image-text representation 500, and thereforeuses few training instances are needed than with conventional VQAsystems due to foundation provided by the image-text representation 500.Therefore, the training process is less resource-intensive as the answerdecoder 502 does not need its own set of training instances.

Turning now to FIG. 5, a visual question answer system 100 is generallyshown in accordance with one or more embodiments of the presentinvention. The system 100 includes a text embedder unit 104 forreceiving a natural language text, for example, a search query from auser, and semantically analyzing the text. The system 100 furtherincludes an image embedder unit 102 for analyzing an image andextracting features from objects in the image. The system 100 furtherincludes a multi-modal encoder unit 106 for combining multiple embeddingvectors into a single vector. The system 100 further includes an answerunit 108 for generating an answer to the query 112 that includes searchquery in relation to an image 110.

The image embedder unit 102 can receive an image 110 from and extractdomain-specific features describing objects in the image 110. The imageembedder unit 102 can further employ a computer vision model to detectand label domain specific-objects in the image 110. The image embedderunit 102 can receive the image 110 as an input and predict a class foreach object contained in the image 110. The image embedder unit 102 canfurther label each object class. The image embedder unit 102 cangenerate a user image embedding vector to represent the extractedfeatures and identified object classes.

The text embedder unit 104 is operable to receive a query 112 inelectronic format from a user computing device 114. The query 112 can bea question from a user requesting information about some aspect of theimage, for example, “What is the most alarming part in this x-ray scan?”The text embedder unit 104 can apply a model that uses natural languageprocessing (NLP) techniques to analyze to query 112 and determine acontext of the query 112. The model can be, for example, a wordembedding model.

The text embedder unit 104 can employ various techniques to derive acontext of the query 112. The text embedder unit 104 can organize thequery 112 into a parse tree to assist in determining the context. Thetext embedder unit 104 can parse the query 112 through various methods,for example, a constituency parsing method. A constituency parsingmethod involves reconstructing a query into a constituency-based parsetree describing the passage's syntactic structure based on a phasestructure grammar. Phase structure grammar is based upon constituencyrelations between tokens as opposed to dependency relations betweentokens. The text embedder unit 104 can also employ a dependency parsingmethod, in which a parse tree is constructed based on a dependencyrelation between tokens. Although only two methods are described, thetext embedder unit 104 can employ various methods to organize a query112 into a parse tree. The text embedder unit 104 can rely on theorganization of the tokens in the text tree to determine a context ofthe query 112. This can be based on words surrounding a target word inthe query, or using a target word to derive the meaning of thesurrounding words. Upon determining a context, the text embedder unit104 can generate a user text embedding vector. The user text embeddingvector is a numeric representation of the respective words and phrasesin the query 112 and denotes the query's semantic meaning.

The multi-modal encoder unit 106 is operable to determine a correlationbetween the user text embedding vector and the user image embeddingvector. The multi-modal encoder unit 106 can map tokens described by theuser text embedding vector to features in the user image embeddingvector. The mapping helps enrich a contextual understanding of the query112. Therefore, if the query 112 is “Should I be concerned with this?”,there would be mapping to an object in the image 110 and it can bedetermined that “this” is in reference to the object, for example, afemur. The multi-modal encoder unit 106 is operable to generate a userjoint representation of the user text embedding vector and the userimage embedding vector. This multi-modal encoder unit 106 can translatethe user joint representation to the same common embedding space as theimage-text representation 500. The user joint representation can be inthe form of a high-dimensional vector.

The answer unit 108 can generate an answer to the query 112. The answerunit 108 generates an answer token by token based at least in part onthe user joint representation and the image-text representation 500. Insome embodiments of the present invention, the answer unit 108 can alterthe image 110 to highlight a target object of the query 112. The answerunit 108 can select an object based on the user joint representation todetermine. The answer unit 108 can further visually alter the object forhighlighting purposes on a user's graphical user interface. For example,the answer unit 108 can alter the image pixels to change a color of theobject, add a border to the object alter the image pixels of the balanceof the image 110 (e.g., blur the rest of the image 110). This allows apotential user to feel confident that a generated answer is in responseto the query 112.

In embodiments of the present invention, the answer unit can furtheralter image pixels of objects related to the query 112, but not thetarget of the query 112. If in the event that a related object is notdetected in the image 110, the answer unit 108 can retrieved and imageof the object from a database and provide an image of the related objectvia the graphical user interface. For example, a query 112 may betargeted to an image of a liver suffering from hepatic encephalopathy(HE). HE can affect the functioning of the nervous system and the brain.The answer unit 108 can be trained to recognize related effects of acondition. In this situation, the answer unit 108 can determine whethereither a nervous system is in the image 110. If so, the answer unit 108can alter the nervous system image for highlighting purposes. Thehighlighting can be distinct from the liver highlighting. If the nervoussystem is not detected in the image 110, the answer unit 108 canretrieve an image of a nervous system and provide an image to the user.The retrieved nervous system image can include the effects of the HE. Inthis sense, both a medical student and a health care professionalreceive information regarding related issues.

As used herein, “machine learning” broadly describes a function ofelectronic systems that learn from data. A machine learning system,engine, or module can include a machine learning algorithm that can betrained, such as in an external cloud environment (e.g., the cloudcomputing environment 50), to learn functional relationships betweeninputs and outputs that are currently unknown. In one or moreembodiments, machine learning functionality can be implemented using anartificial neural network (ANN), having the capability to be trained toperform a currently unknown function. In machine learning and cognitivescience, ANNs are a family of statistical learning models inspired bythe biological neural networks of animals, and in particular, the brain.ANNs can be used to estimate or approximate systems and functions thatdepend on a large number of inputs.

ANNs can be embodied as so-called “neuromorphic” systems ofinterconnected processor elements that act as simulated “neurons” andexchange “messages” between each other in the form of electronicsignals. Similar to the so-called “plasticity” of synapticneurotransmitter connections that carry messages between biologicalneurons, the connections in ANNs that carry electronic messages betweensimulated neurons are provided with numeric weights that correspond tothe strength or weakness of a given connection. The weights can beadjusted and tuned based on experience, making ANNs adaptive to inputsand capable of learning. For example, an ANN for handwriting recognitionis defined by a set of input neurons that can be activated by the pixelsof an input image. After being weighted and transformed by a functiondetermined by the network's designer, the activation of these inputneurons is then passed to other downstream neurons, which are oftenreferred to as “hidden” neurons. This process is repeated until anoutput neuron is activated. The activated output neuron determines whichcharacter was read.

Referring to FIG. 6, a process 600 for training a visual questionanswering system in accordance with one or more embodiments of thepresent invention is shown. It should be appreciated that all or aportion of the processing shown in FIG. 6 can be performed by a computersystem, such as system 100 of FIG. 5. At block 602, an image embedderunit 102 can receive a digital image 202 and a text embedder unit 104can receive an electronic text document 206 as training instances. Thetext document 206 can describe an object(s) in the image 202. In someembodiments of the present invention, the image 202 is a medical image,and the text document 206 is a medical report describing an object inthe image.

At block 604, the image embedder unit 102 can be trained to extractdomain-related features from the image. In some embodiments of thepresent invention, the image embedder unit 102 can be implemented by aneural network. The neural network can execute a model to extractfeatures and classify objects in the image 202. During a training phase,the weights and biases of the neural network can be adjusted to causethe model to extract and classify healthcare related objects. The imageembedder unit 102 can further generate an image embedding vector 204based on the weights and biases. Additionally, the text embedder unit104 can semantically analyze the text document 206. In some embodimentsof the present invention, the text embedder unit 104 can also beimplemented by a neural network. The neural network can execute a modelthat applies natural language processing techniques to determine asemantic meaning of the text document 206. During a training phase, theweights and biases of the neural network can be adjusted to cause themodel to recognize a healthcare related meaning of the text document206. The text embedder unit 104 can further generate a text embeddingvector 208 based on the weights and biases.

At block 606, a multi-modal encoder unit 106 can receive the imageembedding vector 204 and the text embedding vector 208. In someembodiments of the present invention, the multi-modal encoder unit 106can be implemented by a neural network that executes a model. During atraining phase, the weights and biases of the neural network can beadjusted to cause the model to determine a correlation between an objectin the image 202 and a natural language description in the text document206. The multi-modal encoder unit 106 can write data structure to theimage embedding vector 204 and the text embedding vector 208 to generatea mapping between the features of the image 202 and the respectiveportions of the text document 206 that describe the features. The datastructure can be, for example, an mapping. For example, the multi-modalencoder unit 106 can write an function that associates a first portionof the image embedding vector 204 to a portion of the text embeddingvector 208. The multi-modal encoder unit 106 can further use the mappingto generate a joint image-text representation 500 of the image 202 andthe text document.

At block 608, an answer unit 108 can receive an image 110 and a query112 related to the image. In some embodiments of the present invention,the answer unit 108 can be implemented by a neural network that executesa model. The answer unit 108 can be trained to classify objects in theimage 110, and generate a user image embedding vector. The answer unit108 can further be trained to semantically analyze the query 112 anddetermine which object(s) in the image 110 is the query 112 referringto. The answer unit 108 can further generate a user text embeddingvector. The answer unit 108 can map the user text embedding vector tothe object being referred to in the image 110. Based on the mapping, theanswer unit 108 can generate a joint user text-image representation. Thejoint user image-text representation can be translated to a same spaceas the joint image-text representation 500.

At block 610, the answer unit 108 can determine whether the jointimage-text representation 500 references an answer to the query 112. Insome embodiments of the present invention, both the joint userimage-text representation and the joint image-text representation 500can be in the form of respective vectors that relate to a semanticmeaning of each. The answer unit 108 can determine whether the jointuser image-text representation and the joint image-text representation500 are within a threshold distance of each other. If the joint userimage-text representation and the joint image-text representation 500are within a threshold distance, the answer unit 108 can extract anatural language answer from the textual portion of the joint image-textrepresentation 500. The answer unit 108 can further display the answeron the user computing device 114. If the joint user image-textrepresentation and the joint image-text representation 500 are notwithin a threshold distance, the answer unit 108 can compare a distancebetween the joint user image-text representation another the jointimage-text representation.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 7, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 7 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and visual question answering 96.

It is understood that the present disclosure is capable of beingimplemented in conjunction with any other type of computing environmentnow known or later developed. For example, FIG. 9 depicts a blockdiagram of a processing system 900 for implementing the techniquesdescribed herein. In examples, the processing system 900 has one or morecentral processing units (processors) 921 a, 921 b, 921 c, etc.(collectively or generically referred to as processor(s) 921 and/or asprocessing device(s)). In aspects of the present disclosure, eachprocessor 921 can include a reduced instruction set computer (RISC)microprocessor. Processors 921 are coupled to system memory (e.g.,random access memory (RAM) 924) and various other components via asystem bus 933. Read only memory (ROM) 922 is coupled to system bus 933and may include a basic input/output system (BIOS), which controlscertain basic functions of the processing system 900.

Further depicted are an input/output (I/O) adapter 927 and a networkadapter 926 coupled to the system bus 933. I/O adapter 927 may be asmall computer system interface (SCSI) adapter that communicates with ahard disk 923 and/or a storage device 925 or any other similarcomponent. I/O adapter 927, hard disk 923, and storage device 925 arecollectively referred to herein as mass storage 934. Operating system940 for execution on processing system 900 may be stored in mass storage934. The network adapter 926 interconnects system bus 933 with anoutside network 936 enabling processing system 900 to communicate withother such systems.

A display (e.g., a display monitor) 935 is connected to the system bus933 by display adapter 932, which may include a graphics adapter toimprove the performance of graphics intensive applications and a videocontroller. In one aspect of the present disclosure, adapters 926, 927,and/or 932 may be connected to one or more I/O busses that are connectedto the system bus 933 via an intermediate bus bridge (not shown).Suitable I/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 933via user interface adapter 928 and display adapter 932. An input device929 (e.g., a keyboard, a microphone, a touchscreen, etc.), an inputpointer 930 (e.g., a mouse, trackpad, touchscreen, etc.), and/or aspeaker 931 may be interconnected to system bus 933 via user interfaceadapter 928, which may include, for example, a Super I/O chipintegrating multiple device adapters into a single integrated circuit.

In some aspects of the present disclosure, the processing system 900includes a graphics processing unit 937. Graphics processing unit 937 isa specialized electronic circuit designed to manipulate and alter memoryto accelerate the creation of images in a frame buffer intended foroutput to a display. In general, graphics processing unit 937 is veryefficient at manipulating computer graphics and image processing and hasa highly parallel structure that makes it more effective thangeneral-purpose CPUs for algorithms where processing of large blocks ofdata is done in parallel.

Thus, as configured herein, the processing system 900 includesprocessing capability in the form of processors 921, storage capabilityincluding system memory (e.g., RAM 929), and mass storage 934, inputmeans such as keyboard 929 and mouse 930, and output capabilityincluding speaker 931 and display 935. In some aspects of the presentdisclosure, a portion of system memory (e.g., RAM 924) and mass storage934 collectively store the operating system 940 to coordinate thefunctions of the various components shown in the processing system 900.

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

One or more of the methods described herein can be implemented with anyor a combination of the following technologies, which are each wellknown in the art: a discrete logic circuit(s) having logic gates forimplementing logic functions upon data signals, an application specificintegrated circuit (ASIC) having appropriate combinational logic gates,a programmable gate array(s) (PGA), a field programmable gate array(FPGA), etc.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

In some embodiments, various functions or acts can take place at a givenlocation and/or in connection with the operation of one or moreapparatuses or systems. In some embodiments, a portion of a givenfunction or act can be performed at a first device or location, and theremainder of the function or act can be performed at one or moreadditional devices or locations.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” and/or “comprising,”when used in this specification, specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thepresent disclosure has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments were chosen and described in order tobest explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the steps (or operations) described thereinwithout departing from the spirit of the disclosure. For instance, theactions can be performed in a differing order or actions can be added,deleted or modified. Also, the term “coupled” describes having a signalpath between two elements and does not imply a direct connection betweenthe elements with no intervening elements/connections therebetween. Allof these variations are considered a part of the present disclosure.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are understood to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are understood to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection”can include both an indirect “connection” and a direct “connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer-implemented method comprising:extracting, by a processor, a domain-specific object feature from afirst image data, wherein the feature describes an object in the firstimage data; determining, by the processor, domain-specific semanticmeaning of text data; mapping, by the processor, the object feature to aportion of the text data, wherein the portion of the text data describesthe object; creating, by the processor, a joint representation of theobject and the portion of the text data; receiving, by the processor, asecond image data and a query directed towards an object in the secondimage data; and generating, by the processor, an answer to the querybased on the joint representation.
 2. The computer-implemented method ofclaim 1, wherein extracting the domain-specific object featurecomprises: generating a bounding box around the object in the firstimage data; and extracting the object feature from within the boundingbox.
 3. The computer-implemented method of claim 1, wherein determiningthe domain-specific semantic meaning comprises: organizing the text datainto a parse tree, wherein the parse tree is segmented into tokens;masking a token of the segmented tokens in the parse tree; anddetermining a semantic meaning of the masked token based at least inpart on tokens surrounding the masked token.
 4. The computer-implementedmethod of claim 1 further comprising: providing a training image and atraining query; determining an object in the training image associatedwith the training query; and generating a natural language response tothe training query based on the joint representation.
 5. Thecomputer-implemented method of claim 4 further comprising displaying thenatural language response on a display of a user computing device. 6.The computer-implemented method of claim 1, wherein the domain-specificobject feature are extracted by a region-based convolutional neuralnetwork (R-CNN) and the semantic meaning is determined by a recurrentneural network (RNN).
 7. The computer-implemented method of claim 1,wherein the first image data and the text data are related to ahealthcare domain.
 8. A system comprising: a memory having computerreadable instructions; and one or more processors for executing thecomputer readable instructions, the computer readable instructionscontrolling the one or more processors to perform operations comprising:extracting a domain-specific object feature from a first image data,wherein the feature describes an object in the first image data;determining domain-specific semantic meaning of text data; mapping theobject feature to a portion of the text data, wherein the portion of thetext data describes the object; creating a joint representation of theobject and the portion of the text data; receiving a second image dataand a query directed towards an object in the second image data; andgenerating, by the processor, an answer to the query based on the jointrepresentation.
 9. The system of claim 8, wherein extracting thedomain-specific object feature comprises: generating a bounding boxaround the object in the first image data; and extracting the objectfeature from within the bounding box.
 10. The system of claim 8, whereindetermining the domain-specific semantic meaning comprises: organizingthe text data into a parse tree, wherein the parse tree is segmentedinto tokens; masking a token of the segmented tokens in the parse treefrom at least one layer of the neural network; and determining asemantic meaning of the masked token based at least in part on tokenssurrounding the masked token.
 11. The system of claim 8, the operationsfurther comprising: providing the neural network with a training imageand a training query; determining an object in the training imageassociated with the training query; and generating a natural languageresponse to the training query based on the joint representation. 12.The system of claim 11, the operations further comprising displaying thenatural language response on a display of a user computing device. 13.The system of claim 8, wherein the domain-specific object feature areextracted by a region-based convolutional neural network (R-CNN) and thesemantic meaning is determined by a recurrent neural network (RNN). 14.The system of claim 8, wherein the first image data and the text dataare related to a healthcare domain.
 15. A computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor of a neural network to cause the processor to performoperations comprising: extracting a domain-specific object feature froma first image data, wherein the feature describes an object in the firstimage data; determining domain-specific semantic meaning of text data;mapping the object feature to a portion of the text data, wherein theportion of the text data describes the object; creating a jointrepresentation of the object and the portion of the text data; receivinga second image data and a query directed towards an object in the secondimage data; and generating, by the processor, an answer to the querybased on the joint representation.
 16. The computer program product ofclaim 15, wherein extracting the domain-specific object featurecomprises: generating a bounding box around the object in the firstimage data; and extracting the object feature from within the boundingbox.
 17. The computer program product of claim 15, wherein determiningthe domain-specific semantic meaning comprises: organizing the text datainto a parse tree, wherein the parse tree is segmented into tokens;masking a token of the segmented tokens in the parse tree from at leastone layer of the neural network; and determining a semantic meaning ofthe masked token based at least in part on tokens surrounding the maskedtoken.
 18. The computer program product of claim 15, the operationsfurther comprising: providing the neural network with a training imageand a training query; determining an object in the training imageassociated with the training query; and generating a natural languageresponse to the training query based on the joint representation. 19.The computer program product of claim 18, the operations furthercomprising displaying the natural language response on a display of auser computing device.
 20. The computer program product of claim 15,wherein the domain-specific object feature are extracted by aregion-based convolutional neural network (R-CNN) and the semanticmeaning is determined by a recurrent neural network (RNN).